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qa_baselines.py
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import math
import torch
from torch import nn
import numpy as np
from tcomplex import TComplEx
from transformers import RobertaModel
from transformers import BertModel
from transformers import DistilBertModel
import pdb
# training data: questions
# model:
# 1. tkbc model embeddings (may or may not be frozen)
# 2. question sentence embeddings (may or may not be frozen)
# 3. linear layer to project question embeddings (unfrozen)
# 4. transformer that takes these embeddings (unfrozen) (cats them along a dimension, also takes a mask)
# 5. average output embeddings of transformer or take last token embedding?
# 6. linear projection of this embedding to tkbc embedding dimension
# 7. score with all possible entities/times and sigmoid
# 8. BCE loss (multiple correct possible)
class QA_baseline(nn.Module):
def __init__(self, tkbc_model, args):
super().__init__()
self.tkbc_embedding_dim = tkbc_model.embeddings[0].weight.shape[1]
self.sentence_embedding_dim = 768 # hardwired from roberta
if args.model =='bert':
self.pretrained_weights = 'bert-base-uncased'
self.lm_model = BertModel.from_pretrained(self.pretrained_weights)
elif args.model =='roberta':
self.pretrained_weights = 'roberta-base'
self.lm_model = RobertaModel.from_pretrained(self.pretrained_weights)
else:
self.pretrained_weights = 'distilbert-base-uncased'
self.lm_model = DistilBertModel.from_pretrained(self.pretrained_weights)
if args.lm_frozen == 1:
print('Freezing LM params')
for param in self.lm_model.parameters():
param.requires_grad = False
else:
print('Unfrozen LM params')
# creating combined embedding of time and entities (entities come first)
self.tkbc_model = tkbc_model
num_entities = tkbc_model.embeddings[0].weight.shape[0]
num_times = tkbc_model.embeddings[2].weight.shape[0]
ent_emb_matrix = tkbc_model.embeddings[0].weight.data
time_emb_matrix = tkbc_model.embeddings[2].weight.data
full_embed_matrix = torch.cat([ent_emb_matrix, time_emb_matrix], dim=0)
self.entity_time_embedding = nn.Embedding(num_entities + num_times, self.tkbc_embedding_dim)
self.entity_time_embedding.weight.data.copy_(full_embed_matrix)
self.num_entities = num_entities
self.num_times = num_times
if args.frozen == 1:
print('Freezing entity/time embeddings')
self.entity_time_embedding.weight.requires_grad = False
for param in self.tkbc_model.parameters():
param.requires_grad = False
else:
print('Unfrozen entity/time embeddings')
# print('Random starting embedding')
self.linear = nn.Linear(768, self.tkbc_embedding_dim) # to project question embedding
self.linear1 = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
self.linear2 = nn.Linear(self.tkbc_embedding_dim, self.tkbc_embedding_dim)
self.loss = nn.CrossEntropyLoss(reduction='mean')
self.dropout = torch.nn.Dropout(0.3)
self.bn1 = torch.nn.BatchNorm1d(self.tkbc_embedding_dim)
self.bn2 = torch.nn.BatchNorm1d(self.tkbc_embedding_dim)
return
def getQuestionEmbedding(self, question_tokenized, attention_mask):
lm_last_hidden_states = self.lm_model(question_tokenized, attention_mask=attention_mask)[0]
states = lm_last_hidden_states.transpose(1,0)
cls_embedding = states[0]
question_embedding = cls_embedding
# question_embedding = torch.mean(roberta_last_hidden_states, dim=1)
return question_embedding
class QA_lm(QA_baseline):
def __init__(self, tkbc_model, args):
super().__init__(tkbc_model, args)
self.final_linear = nn.Linear(4*self.tkbc_embedding_dim, self.tkbc_embedding_dim)
return
def forward(self, a):
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
heads = a[2].cuda()
tails = a[3].cuda()
times = a[4].cuda()
head_embedding = self.entity_time_embedding(heads)
tail_embedding = self.entity_time_embedding(tails)
time_embedding = self.entity_time_embedding(times)
question_embedding = self.getQuestionEmbedding(question_tokenized, question_attention_mask)
relation_embedding = self.linear(question_embedding)
output = self.final_linear(torch.cat((head_embedding, relation_embedding, tail_embedding, time_embedding), dim=-1))
scores = torch.matmul(output, self.entity_time_embedding.weight.data.T)
return scores
class QA_embedkgqa(QA_baseline):
def __init__(self, tkbc_model, args):
super().__init__(tkbc_model, args)
return
def score(self, head_embedding, relation_embedding):
lhs = head_embedding
rel = relation_embedding
right = torch.cat((self.entity_embedding.weight, self.time_embedding.weight), dim=0)
lhs = lhs[:, :self.rank], lhs[:, self.rank:]
rel = rel[:, :self.rank], rel[:, self.rank:]
right = right[:, :self.rank], right[:, self.rank:]
return (lhs[0] * rel[0] - lhs[1] * rel[1]) @ right[0].transpose(0, 1) + (lhs[0] * rel[1] + lhs[1] * rel[0]) @ right[1].transpose(0, 1)
def forward(self, a):
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
heads = a[2].cuda()
head_embedding = self.entity_embedding(heads)
question_embedding = self.getQuestionEmbedding(question_tokenized, question_attention_mask)
relation_embedding = self.linear(question_embedding)
relation_embedding1 = self.dropout(self.bn1(self.linear1(relation_embedding)))
scores = self.score(head_embedding, relation_embedding1)
# exit(0)
# scores = torch.cat((scores_entity, scores_time), dim=1)
return scores
class QA_cronkgqa(QA_baseline):
def __init__(self, tkbc_model, args):
super().__init__(tkbc_model, args)
self.supervision = args.supervision
return
def infer_time(self, head_embedding, tail_embedding, relation_embedding):
lhs = head_embedding
rhs = tail_embedding
rel = relation_embedding
time = self.tkbc_model.embeddings[2].weight # + self.tkbc_model.lin2(self.tkbc_model.time_embedding.weight)
# time = self.entity_time_embedding.weight
lhs = lhs[:, :self.tkbc_model.rank], lhs[:, self.tkbc_model.rank:]
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
rhs = rhs[:, :self.tkbc_model.rank], rhs[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
return torch.cat([
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]),
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1])], dim=-1
)
# scoring function from TComplEx
def score_time(self, head_embedding, tail_embedding, relation_embedding):
lhs = head_embedding
rhs = tail_embedding
rel = relation_embedding
time = self.tkbc_model.embeddings[2].weight
# time = self.entity_time_embedding.weight
lhs = lhs[:, :self.tkbc_model.rank], lhs[:, self.tkbc_model.rank:]
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
rhs = rhs[:, :self.tkbc_model.rank], rhs[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
return (
(lhs[0] * rel[0] * rhs[0] - lhs[1] * rel[1] * rhs[0] -
lhs[1] * rel[0] * rhs[1] + lhs[0] * rel[1] * rhs[1]) @ time[0].t() +
(lhs[1] * rel[0] * rhs[0] - lhs[0] * rel[1] * rhs[0] +
lhs[0] * rel[0] * rhs[1] - lhs[1] * rel[1] * rhs[1]) @ time[1].t()
)
def score_entity(self, head_embedding, tail_embedding,relation_embedding, time_embedding):
lhs = head_embedding[:, :self.tkbc_model.rank], head_embedding[:, self.tkbc_model.rank:]
rel = relation_embedding
time = time_embedding
rel = rel[:, :self.tkbc_model.rank], rel[:, self.tkbc_model.rank:]
time = time[:, :self.tkbc_model.rank], time[:, self.tkbc_model.rank:]
right = self.tkbc_model.embeddings[0].weight
# right = self.entity_time_embedding.weight
right = right[:, :self.tkbc_model.rank], right[:, self.tkbc_model.rank:]
rt = rel[0] * time[0], rel[1] * time[0], rel[0] * time[1], rel[1] * time[1]
full_rel = rt[0] - rt[3], rt[1] + rt[2]
return (
(lhs[0] * full_rel[0] - lhs[1] * full_rel[1]) @ right[0].t() +
(lhs[1] * full_rel[0] + lhs[0] * full_rel[1]) @ right[1].t()
)
def forward(self, a):
question_tokenized = a[0].cuda()
question_attention_mask = a[1].cuda()
heads = a[2].cuda()
tails = a[3].cuda()
times = a[4].cuda()
head_embedding = self.entity_time_embedding(heads)
tail_embedding = self.entity_time_embedding(tails)
time_embedding = self.entity_time_embedding(times)
question_embedding = self.getQuestionEmbedding(question_tokenized, question_attention_mask)
relation_embedding = self.linear(question_embedding)
if self.supervision == 'soft':
t1_emb = self.infer_time(head_embedding, tail_embedding, relation_embedding)
t2_emb = self.infer_time(tail_embedding, head_embedding, relation_embedding)
time_embedding = (time_embedding + t1_emb + t2_emb)/3 #just the mean
relation_embedding1 = self.dropout(self.bn1(self.linear1(relation_embedding)))
relation_embedding2 = self.dropout(self.bn2(self.linear2(relation_embedding)))
scores_time = self.score_time(head_embedding, tail_embedding, relation_embedding1)
scores_entity = self.score_entity(head_embedding, tail_embedding,relation_embedding2, time_embedding)
scores = torch.cat((scores_entity, scores_time), dim=1)
return scores